CVE-2022-23593: TensorFlow DoS — HIGH

CISO Take

Any TensorFlow 2.7.x deployment using MLIR-TFRT JIT compilation can be crashed remotely with no authentication required by sending scalar-shaped tensors — CVSS 7.5 with zero complexity. Patch to TensorFlow 2.8.0 immediately; if patching is delayed, isolate TF serving endpoints from untrusted network input. This is a single affected version with a confirmed fix, making remediation straightforward.

What is the risk?

High risk for internet-exposed TensorFlow inference services. CVSS AV:N/AC:L/PR:N/UI:N means any unauthenticated network attacker can trigger the crash with minimal effort — no AI/ML expertise required, just a scalar-shaped tensor input. Impact is pure availability: no data exfiltration path. The blast radius is limited to deployments specifically using the MLIR-TFRT JIT compilation path (TF 2.7.x only), but those running ML-as-a-Service with public endpoints face real disruption risk.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
TensorFlow	pip	—	No patch
195.8K OpenSSF 7.1 3.7K dependents Pushed 3d ago 4% patched ~1372d to patch Full package profile →

Do you use TensorFlow? You're affected.

How severe is it?

CVSS 3.1

7.5 / 10

EPSS

0.9%

chance of exploitation in 30 days

Higher than 54% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

What should I do?

5 steps

PATCH

Upgrade to TensorFlow 2.8.0 (contains the fix) or apply commit 35f0fabb4c178253a964d7aabdbb15c6a398b69a.
WORKAROUND (if immediate patching not possible): Add input validation to reject or reshape scalar tensors before they reach MLIR-TFRT compilation.
NETWORK CONTROLS

Place TF Serving endpoints behind an API gateway that enforces minimum tensor rank requirements.
DETECTION

Monitor for abnormal process terminations or segfaults (SIGSEGV) in TensorFlow serving processes — alert on unexpected model server restarts.
VERIFY

Confirm your TF deployment version with import tensorflow as tf; print(tf.__version__) — only 2.7.x is affected.

How is it classified?

DoS Framework Inference AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.5 - AI system availability and resilience

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain the value of AI systems MAP-5.1 - Likelihood of undesirable outcomes examined

OWASP LLM Top 10

LLM05:2025 - Insecure Output Handling / Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2022-23593?

Any TensorFlow 2.7.x deployment using MLIR-TFRT JIT compilation can be crashed remotely with no authentication required by sending scalar-shaped tensors — CVSS 7.5 with zero complexity. Patch to TensorFlow 2.8.0 immediately; if patching is delayed, isolate TF serving endpoints from untrusted network input. This is a single affected version with a confirmed fix, making remediation straightforward.

Is CVE-2022-23593 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-23593, increasing the risk of exploitation.

How to fix CVE-2022-23593?

1. PATCH: Upgrade to TensorFlow 2.8.0 (contains the fix) or apply commit 35f0fabb4c178253a964d7aabdbb15c6a398b69a. 2. WORKAROUND (if immediate patching not possible): Add input validation to reject or reshape scalar tensors before they reach MLIR-TFRT compilation. 3. NETWORK CONTROLS: Place TF Serving endpoints behind an API gateway that enforces minimum tensor rank requirements. 4. DETECTION: Monitor for abnormal process terminations or segfaults (SIGSEGV) in TensorFlow serving processes — alert on unexpected model server restarts. 5. VERIFY: Confirm your TF deployment version with `import tensorflow as tf; print(tf.__version__)` — only 2.7.x is affected.

What systems are affected by CVE-2022-23593?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, inference infrastructure.

What is the CVSS score for CVE-2022-23593?

CVE-2022-23593 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.86%.

What is the AI security impact?

Affected AI Architectures

model servingtraining pipelinesinference infrastructure

MITRE ATLAS Techniques

AML.T0010.001 AI Software

AML.T0029 Denial of AI Service

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.6.2.5

NIST AI RMF: MANAGE-2.2, MAP-5.1

OWASP LLM Top 10: LLM05:2025

What are the technical details?

Original Advisory

Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.

Exploitation Scenario

Attacker discovers a public-facing TensorFlow Serving endpoint (e.g., a model inference API). They craft a gRPC or REST inference request with scalar (rank-0) tensors as input — trivially constructed with any TF client. The request triggers the `simplifyBroadcast` optimization pass in MLIR-TFRT; with all shapes being scalar, `maxRank` is 0 and an empty SmallVector is built without bounds checking, causing a segfault. The TF Serving process crashes, taking the inference API offline. The attacker can loop this at low cost to maintain a persistent DoS against any ML service behind that endpoint.

Weaknesses (CWE)

CWE-754 Improper Check for Unusual or Exceptional Conditions Primary CWE-754 Improper Check for Unusual or Exceptional Conditions

CWE-754 — Improper Check for Unusual or Exceptional Conditions: The product does not check or incorrectly checks for unusual or exceptional conditions that are not expected to occur frequently during day to day operation of the product.

[Requirements] Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. Choose languages with features such as exception handling that force the programmer to anticipate unusual conditions that may generate exceptions. Custom exceptions may need to be developed to handle unusual business-logic conditions. Be careful not to pass sensitive exceptions back to the user (CWE-209, CWE-248).
[Implementation] Check the results of all functions that return a value and verify that the value is expected.

Source: MITRE CWE corpus.